FictionEro - Data Cleaning

Data Preparation

Code
library(tidyverse)
library(easystats)
library(patchwork)
library(ggside)
df <- read.csv("../data/rawdata_participants.csv") |> 
  mutate(across(everything(), ~ifelse(.x == "", NA, .x))) |>
  mutate(Experimenter = case_when(
    Language=="English" & Experimenter %in% c("reddit7", "reddit8", "reddit1", "reddit2", "reddit5") ~ "Reddit (other)",
    .default = Experimenter
  ))

dftask <- read.csv("../data/rawdata_task.csv") |> 
  full_join(
    df[c("Participant", "Sex", "SexualOrientation")],
    by = join_by(Participant)
    )

The initial sample consisted of 556 participants (Mean age = 32.7, SD = 12.5, range: [18, 80]; Sex: 32.2% females, 66.9% males, 0.9% other; Education: Bachelor, 36.87%; Doctorate, 6.29%; High School, 32.55%; Master, 21.76%; Other, 1.98%; Primary School, 0.54%; Country: 21.58% USA, 18.53% UK, 15.29% Colombia, 11.33% France, 33.27% other).

Compute Scores

# Create Sexual "relevance" variable (Relevant, irrelevant, non-erotic)
dftask <- dftask |> 
  mutate(Relevance = case_when(
    Type == "Non-erotic" ~ "Non-erotic",
    Sex == "Male" & SexualOrientation == "Heterosexual" & Category == "Female" ~ "Relevant",
    Sex == "Female" & SexualOrientation == "Heterosexual" & Category == "Male" ~ "Relevant",
    Sex == "Male" & SexualOrientation == "Homosexual" & Category == "Male" ~ "Relevant",
    Sex == "Female" & SexualOrientation == "Homosexual" & Category == "Female" ~ "Relevant",
    # TODO: what to do with "Other"? 
    SexualOrientation %in% c("Bisexual", "Other") & Category %in% c("Male", "Female") ~ "Relevant",
    .default = "Irrelevant"
  )) 

Recruitment History

Code
plot_recruitement <- function(df) {
  # Consecutive count of participants per day (as area)
  df |>
    mutate(Date = as.Date(Date, format = "%d/%m/%Y")) |> 
    group_by(Date, Language, Experimenter) |> 
    summarize(N = n()) |> 
    ungroup() |>
    # https://bocoup.com/blog/padding-time-series-with-r
    complete(Date, Language, Experimenter, fill = list(N = 0)) |> 
    group_by(Language, Experimenter) |>
    mutate(N = cumsum(N)) |>
    ggplot(aes(x = Date, y = N)) +
    geom_area(aes(fill=Experimenter)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(
      title = "Recruitment History",
      x = "Date",
      y = "Total Number of Participants"
    ) +
    see::theme_modern() 
}

plot_recruitement(df) +
  facet_wrap(~Language, nrow=5, scales = "free_y")

Code
# Table
table_recruitment <- function(df) {
  summarize(df, N = n(), .by=c("Language", "Experimenter")) |> 
    arrange(desc(N)) |> 
    gt::gt() |> 
    gt::opt_stylize() |> 
    gt::opt_interactive(use_compact_mode = TRUE) |> 
    gt::tab_header("Number of participants per recruitment source")
}
table_recruitment(df)
Number of participants per recruitment source
Code
plot_recruitement(filter(df, Language == "English"))

Code
table_recruitment(filter(df, Language == "English"))
Number of participants per recruitment source
Code
plot_recruitement(filter(df, Language == "French"))

Code
table_recruitment(filter(df, Language == "French"))
Number of participants per recruitment source
Code
plot_recruitement(filter(df, Language == "Italian"))

Code
table_recruitment(filter(df, Language == "Italian"))
Number of participants per recruitment source
Code
plot_recruitement(filter(df, Language == "Colombian"))

Code
table_recruitment(filter(df, Language == "Colombian"))
Number of participants per recruitment source

Feedback

Evaluation

The majority of participants found the study to be a “fun” experience. Interestingly, reports of “fun” were significantly associated with finding at least some stimuli arousing. Conversely, reporting “no feelings” was associated with finding the experiment “boring”.

Code
df |> 
  select(starts_with("Feedback"), -Feedback_Comments) |>
  pivot_longer(everything(), names_to = "Question", values_to = "Answer") |>
  group_by(Question, Answer) |> 
  summarise(prop = n()/nrow(df), .groups = 'drop') |> 
  complete(Question, Answer, fill = list(prop = 0)) |> 
  filter(Answer == "True") |> 
  mutate(Question = str_remove(Question, "Feedback_"),
         Question = str_replace(Question, "AILessArousing", "AI = Less arousing"),
         Question = str_replace(Question, "AIMoreArousing", "AI = More arousing"),
         Question = str_replace(Question, "CouldNotDiscriminate", "Hard to discriminate"),
         Question = str_replace(Question, "LabelsIncorrect", "Labels were incorrect"),
         Question = str_replace(Question, "NoFeels", "Didn't feel anything"),
         Question = str_replace(Question, "CouldDiscriminate", "Easy to discriminate"),
         Question = str_replace(Question, "LabelsReversed", "Labels were reversed")) |>
  mutate(Question = fct_reorder(Question, desc(prop))) |> 
  ggplot(aes(x = Question, y = prop)) +
  geom_bar(stat = "identity") +
  scale_y_continuous(expand = c(0, 0), breaks= scales::pretty_breaks(), labels=scales::percent) +
  labs(x="Feedback", y = "Participants", title = "Feedback") +
  theme_modern(axis.title.space = 15) +
  theme(
    plot.title = element_text(size = rel(1.2), face = "bold", hjust = 0),
    plot.subtitle = element_text(size = rel(1.2), vjust = 7),
    axis.text.y = element_text(size = rel(1.1)),
    axis.text.x = element_text(size = rel(1.1), angle = 45, hjust = 1),
    axis.title.x = element_blank()
  )

Code
cor <- df |> 
  select(starts_with("Feedback"), -Feedback_Comments) |> 
  mutate_all(~ifelse(.=="True", 1, 0)) |> 
  correlation(method="tetrachoric", redundant = TRUE) |> 
  correlation::cor_sort() |> 
  correlation::cor_lower()

cor |> 
  mutate(val = paste0(insight::format_value(rho), format_p(p, stars_only=TRUE))) |>
  mutate(Parameter2 = fct_rev(Parameter2)) |>
  mutate(Parameter1 = fct_relabel(Parameter1, \(x) str_remove_all(x, "Feedback_")),
         Parameter2 = fct_relabel(Parameter2, \(x) str_remove_all(x, "Feedback_"))) |>
  ggplot(aes(x=Parameter1, y=Parameter2)) +
  geom_tile(aes(fill = rho), color = "white") +
  geom_text(aes(label = val), size = 3) +
  labs(title = "Feedback Co-occurence Matrix") +
  scale_fill_gradient2(
    low = "#2196F3",
    mid = "white",
    high = "#F44336",
    breaks = c(-1, 0, 1),
    guide = guide_colourbar(ticks=FALSE),
    midpoint = 0,
    na.value = "grey85",
    limit = c(-1, 1))  + 
  theme_minimal() +
  theme(legend.title = element_blank(),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1))

Comments

Code
data.frame(Language = df$Language,
           Source = df$Experimenter,
           Comments = trimws(df$Feedback_Comments)) |> 
  filter(!tolower(Comments) %in% c(NA, "no", "nope", "none", "na", "n/a", "non")) |> 
  arrange(Language, Source) |>
  gt::gt() |> 
  gt::opt_stylize() |> 
  gt::opt_interactive(use_compact_mode = TRUE) 

Exclusion

outliers <- c(
  # "S206"  # Collapsed RTs in both phases
  # "S399"  # Negative Arousal-Valence correlations
  "S428",  # Only 0s for arousal (creates statistical problems)
  "S498",  # Only 0s for arousal (creates statistical problems)
  "S508"  # Only 0s for arousal (creates statistical problems)
  )
potentials <- list()

Mobile

Code
df |>
  ggplot(aes(x=Mobile, fill=Language)) +
  geom_bar() +
  geom_hline(yintercept=0.5*nrow(df), linetype="dashed") +
  theme_modern()

We removed 153 participants that participated with a mobile device.

Code
df <- filter(df, Mobile == "False")
dftask <- filter(dftask, Participant %in% df$Participant)

Experiment Duration

The experiment’s median duration is 24.85 min (50% CI [18.31, 26.17]).

Code
df |>
  mutate(Participant = fct_reorder(Participant, Experiment_Duration),
         Category = ifelse(Experiment_Duration > 60, "extra", "ok"),
         Duration = ifelse(Experiment_Duration > 60, 60, Experiment_Duration),
         Group = ifelse(Participant %in% outliers, "Outlier", "ok")) |>
  ggplot(aes(y = Participant, x = Duration)) +
  geom_point(aes(color = Group, shape = Category)) +
  geom_vline(xintercept = median(df$Experiment_Duration), color = "red", linetype = "dashed") +
  scale_shape_manual(values = c("extra" = 3, ok = 19)) +
  scale_color_manual(values = c("Outlier" = "red", ok = "black"), guide="none") +
  guides(color = "none", shape = "none") +
  ggside::geom_xsidedensity(fill = "#4CAF50", color=NA) +
  ggside::scale_xsidey_continuous(expand = c(0, 0)) +
  labs(
    title = "Experiment Completion Time",
    x = "Duration (in minutes)",
    y = "Participant"
  )  +
  theme_bw() +
  ggside::theme_ggside_void() +
  theme(ggside.panel.scale = .3,
        panel.border = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank())

Code
potentials$expe_duration <- arrange(df, Experiment_Duration) |>
  select(Participant, Experiment_Duration) |>
  head(5) 

Task Duration

Code
plot_hist <- function(dat) {
  dens <- rbind(
    mutate(bayestestR::estimate_density(filter(dftask, RT1 < 40 & RT2 < 40)$RT1), 
           Phase="Emotional ratings",
           y = y / max(y)),
    mutate(bayestestR::estimate_density(filter(dftask, RT1 < 40 & RT2 < 40)$RT2), 
           Phase="Reality rating",
           y = y / max(y))
  )
  
  dat |> 
    filter(RT1 < 40 & RT2 < 40) |>  # Remove very long RTs
    # mutate(Participant = fct_reorder(Participant, RT1)) |> 
    pivot_longer(cols = c(RT1, RT2), names_to = "Phase", values_to = "RT") |>
    mutate(Phase = ifelse(Phase == "RT1", "Emotional ratings", "Reality rating")) |>
    ggplot(aes(x=RT)) +
    geom_area(data=dens, aes(x=x, y=y, fill=Phase), alpha=0.33, position="identity") +
    geom_density(aes(color=Phase, y=after_stat(scaled)), linewidth=1.5) + 
    scale_x_sqrt(breaks=c(0, 2, 5, 10, 20)) +
    theme_minimal() +
    theme(axis.title.y = element_blank(),
          axis.ticks.y = element_blank(),
          axis.text.y = element_blank(),
          axis.line.y = element_blank()) +
    labs(title = "Distribution of Response Time for each Participant", x="Response time per stimuli (s)") +
    facet_wrap(~Participant, ncol=5, scales="free_y") +
    coord_cartesian(xlim = c(0, 25))
}
Code
plot_hist(dftask[dftask$Participant %in% df$Participant[1:60], ])

Code
plot_hist(dftask[dftask$Participant %in% df$Participant[61:120], ])

Code
plot_hist(dftask[dftask$Participant %in% df$Participant[121:180], ])

Code
plot_hist(dftask[dftask$Participant %in% df$Participant[181:240], ])

Code
plot_hist(dftask[dftask$Participant %in% df$Participant[241:264], ])

BAIT Questionnaire Duration

Code
df |>
  mutate(Participant = fct_reorder(Participant, BAIT_Duration),
         Category = ifelse(BAIT_Duration > 5, "extra", "ok"),
         Duration = ifelse(BAIT_Duration > 5, 5, BAIT_Duration),
         Group = ifelse(Participant %in% outliers, "Outlier", "ok")) |>
  ggplot(aes(y = Participant, x = Duration)) +
  geom_point(aes(color = Group, shape = Category)) +
  geom_vline(xintercept = median(df$BAIT_Duration), color = "red", linetype = "dashed") +
  scale_shape_manual(values = c("extra" = 3, ok = 19)) +
  scale_color_manual(values = c("Outlier" = "red", ok = "black"), guide="none") +
  guides(color = "none", shape = "none") +
  ggside::geom_xsidedensity(fill = "#9C27B0", color=NA) +
  ggside::scale_xsidey_continuous(expand = c(0, 0)) +
  labs(
    title = "Questionnaire Completion Time",
    x = "Duration (in minutes)",
    y = "Participant"
  )  +
  theme_bw() +
  ggside::theme_ggside_void() +
  theme(ggside.panel.scale = .3,
        panel.border = element_blank(),
        axis.ticks.y = element_blank(),
          axis.text.y = element_blank()) 

Response to Erotic Stimuli

Code
dat <- dftask |> 
  filter(Relevance %in% c("Relevant", "Non-erotic")) |> 
  group_by(Participant, Type) |> 
  summarise(Arousal = mean(Arousal), 
            Valence = mean(Valence),
            Enticement = mean(Enticement),
            .groups = "drop") |>
  pivot_wider(names_from = Type, values_from = all_of(c("Arousal", "Valence", "Enticement"))) |>
  transmute(Participant = Participant,
            Arousal = Arousal_Erotic - `Arousal_Non-erotic`,
            Valence = Valence_Erotic - `Valence_Non-erotic`,
            Enticement = Enticement_Erotic - `Enticement_Non-erotic`) |>
  filter(!is.na(Arousal)) |> 
  mutate(Participant = fct_reorder(Participant, Arousal)) 

dat |> 
  pivot_longer(-Participant) |> 
  mutate(Group = ifelse(Participant %in% outliers, "Outlier", "ok")) |> 
  ggplot(aes(x=value, y=Participant, fill=Group)) +
  geom_bar(aes(fill=value), stat = "identity") +
  scale_fill_gradient2(low = "#3F51B5", mid = "#FF9800", high = "#4CAF50", midpoint = 0) +
  # scale_fill_manual(values = c("Outlier" = "red", ok = "black"), guide="none") +
  theme_bw() +
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank()) +
  labs(title = "Difference between Erotic and Neutral", x="Erotic - Neutral") +
  facet_wrap(~name, ncol=3, scales="free_x")

Code
potentials$emo_diff <- arrange(dat, Arousal) |>
  head(5)

Response Coherence

Code
# dftask[dftask$Participant == "S428", "Arousal"]
# dftask[dftask$Participant == "S498", "Arousal"]
# dftask[dftask$Participant == "S508", "Arousal"]
dat <- dftask |> 
  filter(!Participant %in% c("S428", "S498", "S508")) |> 
  summarize(cor_ArVal = cor(Arousal, Valence),
            cor_ArEnt = cor(Arousal, Enticement),
            .by="Participant") 
  
dat |>
  left_join(df[c("Participant", "Language")], by="Participant") |>
  mutate(Participant = fct_reorder(Participant, cor_ArVal))  |> 
  pivot_longer(starts_with("cor_")) |> 
  mutate(Group = ifelse(Participant %in% outliers, "Outlier", "ok")) |> 
  mutate(name = fct_relevel(name, "cor_ArVal", "cor_ArEnt"),
         name = fct_recode(name, "Arousal - Valence" = "cor_ArVal", "Arousal - Enticement" = "cor_ArEnt")) |>
  ggplot(aes(y = Participant, x = value)) +
  geom_bar(aes(fill = Language), stat = "identity") +
  # scale_fill_gradient2(low = "#3F51B5", mid = "#FF9800", high = "#4CAF50", midpoint = 0) +
  # scale_fill_manual(values = c("Outlier" = "red", ok = "black"), guide="none") +
  theme_bw() +
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank()) +
  labs(title = "Difference between Erotic and Neutral", x="Erotic - Neutral") +
  facet_wrap(~name, ncol=3, scales="free_x")

Code
potentials$emo_cor <- arrange(dat, cor_ArVal) |>
  head(5)
Code
c(as.character(potentials$expe_duration$Participant), 
  as.character(potentials$emo_diff$Participant), 
  as.character(potentials$emo_cor$Participant)) |> 
  table()

S203 S256 S297 S385 S393 S403 S416 S422 S429 S430 S446 S456 S466 S540 S541 
   1    1    1    1    1    1    1    1    1    1    1    1    1    1    1 

Sexual Profile

Sample

Code
df |>
  ggplot(aes(x = Sex)) +
  geom_bar(aes(fill = SexualOrientation)) +
  scale_y_continuous(expand = c(0, 0), breaks = scales::pretty_breaks()) +
  scale_fill_metro_d() +
  labs(x = "Biological Sex", y = "Number of Participants", title = "Sex and Sexual Orientation", fill = "Sexual Orientation") +
  theme_modern(axis.title.space = 15) +
  theme(
    plot.title = element_text(size = rel(1.2), face = "bold", hjust = 0),
    plot.subtitle = element_text(size = rel(1.2), vjust = 7),
    axis.text.y = element_text(size = rel(1.1)),
    axis.text.x = element_text(size = rel(1.1)),
    axis.title.x = element_blank()
  )

We removed 15 participants that were incompatible with further analysis.

df <- filter(df, Sex != "Other" & SexualOrientation != "Other")
dftask <- filter(dftask, Participant %in% df$Participant)

Task Behaviour

Code
show_distribution <- function(dftask, target="Arousal") {
  dftask |> 
    filter(SexualOrientation %in% c("Heterosexual", "Bisexual", "Homosexual")) |>
    bayestestR::estimate_density(select=target, 
                                 at=c("Relevance", "Category", "Sex", "SexualOrientation"), 
                                 method="KernSmooth") |>
    ggplot(aes(x = x, y = y)) +
    geom_line(aes(color = Relevance, linetype = Category), linewidth=1) +
    facet_grid(SexualOrientation~Sex, scales="free_y")  +
    scale_color_manual(values = c("Relevant" = "red", "Non-erotic" = "blue", "Irrelevant"="darkorange")) +
    scale_y_continuous(expand = c(0, 0)) +
    scale_x_continuous(expand = c(0, 0)) +
    theme_minimal()  +
    theme(axis.title.x = element_blank(),
          axis.title.y = element_blank(),
          axis.text.y = element_blank(),
          plot.title = element_text(face="bold")) +
    labs(title = target) 
}

(show_distribution(dftask, "Arousal") | show_distribution(dftask, "Valence")) /
  (show_distribution(dftask, "Enticement") | show_distribution(dftask, "Realness")) +
  patchwork::plot_layout(guides = "collect") +
  patchwork::plot_annotation(title = "Distribution of Appraisals depending on the Sexual Profile",
                             theme = theme(plot.title = element_text(hjust = 0.5, face="bold"))) 

We kept only heterosexual participants (76.29%).

df <- filter(df, SexualOrientation == "Heterosexual")
dftask <- filter(dftask, Participant %in% df$Participant)

Final Sample

Code
df <- filter(df, !Participant %in% outliers)
dftask <- filter(dftask, Participant %in% df$Participant)

The final sample includes 293 participants (Mean age = 34.0, SD = 13.4, range: [18, 80]; Sex: 32.1% females, 67.9% males, 0.0% other; Education: Bachelor, 34.47%; Doctorate, 7.17%; High School, 30.38%; Master, 25.26%; Other, 2.39%; Primary School, 0.34%; Country: 21.16% Colombia, 18.77% USA, 15.70% UK, 12.63% France, 31.74% other).

Code
p_country <- dplyr::select(df, region = Country) |>
  group_by(region) |>
  summarize(n = n()) |>
  right_join(map_data("world"), by = "region") |>
  ggplot(aes(long, lat, group = group)) +
  geom_polygon(aes(fill = n)) +
  scale_fill_gradientn(colors = c("#FFEB3B", "red", "purple")) +
  labs(fill = "N") +
  theme_void() +
  labs(title = "A Geographically Diverse Sample", subtitle = "Number of participants by country")  +
  theme(
    plot.title = element_text(size = rel(1.2), face = "bold", hjust = 0),
    plot.subtitle = element_text(size = rel(1.2))
  )
p_country

Code
ggwaffle::waffle_iron(df, ggwaffle::aes_d(group = Ethnicity), rows=10) |> 
  ggplot(aes(x, y, fill = group)) + 
  ggwaffle::geom_waffle() + 
  coord_equal() + 
  scale_fill_flat_d() + 
  ggwaffle::theme_waffle() +
  labs(title = "Self-reported Ethnicity", subtitle = "Each square represents a participant", fill="")  +
  theme(
    plot.title = element_text(size = rel(1.2), face = "bold", hjust = 0),
    plot.subtitle = element_text(size = rel(1.2)),
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  )
Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.

Code
p_age <- estimate_density(df$Age) |>
  normalize(select = y) |> 
  mutate(y = y * 86) |>  # To match the binwidth
  ggplot(aes(x = x)) +
  geom_histogram(data=df, aes(x = Age), fill = "#616161", bins=28) +
  # geom_line(aes(y = y), color = "orange", linewidth=2) +
  geom_vline(xintercept = mean(df$Age), color = "red", linewidth=1.5) +
  # geom_label(data = data.frame(x = mean(df$Age) * 1.15, y = 0.95 * 75), aes(y = y), color = "red", label = paste0("Mean = ", format_value(mean(df$Age)))) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  labs(title = "Age", y = "Number of Participants", color = NULL, subtitle = "Distribution of participants' age") +
  theme_modern(axis.title.space = 10) +
  theme(
    plot.title = element_text(size = rel(1.2), face = "bold", hjust = 0),
    plot.subtitle = element_text(size = rel(1.2), vjust = 7),
    axis.text.y = element_text(size = rel(1.1)),
    axis.text.x = element_text(size = rel(1.1)),
    axis.title.x = element_blank()
  )
p_age

Code
p_edu <- df |>
  mutate(Education = fct_relevel(Education, "Other", "Primary School", "High School", "Bachelor", "Master", "Doctorate")) |> 
  ggplot(aes(x = Education)) +
  geom_bar(aes(fill = Education)) +
  scale_y_continuous(expand = c(0, 0), breaks= scales::pretty_breaks()) +
  scale_fill_viridis_d(guide = "none") +
  labs(title = "Education", y = "Number of Participants", subtitle = "Participants per achieved education level") +
  theme_modern(axis.title.space = 15) +
  theme(
    plot.title = element_text(size = rel(1.2), face = "bold", hjust = 0),
    plot.subtitle = element_text(size = rel(1.2), vjust = 7),
    axis.text.y = element_text(size = rel(1.1)),
    axis.text.x = element_text(size = rel(1.1)),
    axis.title.x = element_blank()
  )
p_edu

Birth Control

Code
colors <- c(
  "NA" = "#2196F3", "None" = "#E91E63", "Condoms (for partner)" = "#9C27B0",
  "Combined pills" = "#FF9800", "Intrauterine Device (IUD)" = "#FF5722", 
  "Intrauterine System (IUS)" = "#795548", "Progestogen pills" = "#4CAF50",
  "Other" = "#FFC107", "Condoms (female)" = "#607D8B"
)
colors <- colors[names(colors) %in% c("NA", df$BirthControl)]

p_sex <- df |>
  mutate(BirthControl = ifelse(Sex == "Male", "NA", BirthControl),
         BirthControl = fct_relevel(BirthControl, names(colors))) |>
  ggplot(aes(x = Sex)) +
  geom_bar(aes(fill = BirthControl)) +
  scale_y_continuous(expand = c(0, 0), breaks = scales::pretty_breaks()) +
  scale_fill_manual(
    values = colors,
    breaks = names(colors)[2:length(colors)]
  ) +
  labs(x = "Biological Sex", y = "Number of Participants", title = "Sex and Birth Control Method", fill = "Birth Control") +
  theme_modern(axis.title.space = 15) +
  theme(
    plot.title = element_text(size = rel(1.2), face = "bold", hjust = 0),
    plot.subtitle = element_text(size = rel(1.2), vjust = 7),
    axis.text.y = element_text(size = rel(1.1)),
    axis.text.x = element_text(size = rel(1.1)),
    axis.title.x = element_blank()
  )
p_sex

Sexual Profile

Code
p_sexprofile <- df |>
  select(Participant, Sex, SexualOrientation, SexualActivity, COPS_Duration_1, COPS_Frequency_2) |> 
  pivot_longer(-all_of(c("Participant", "Sex"))) |> 
  mutate(name = str_replace_all(name, "SexualOrientation", "Sexual Orientation"),
         name = str_replace_all(name, "SexualActivity", "Sexual Activity"),
         name = str_replace_all(name, "COPS_Duration_1", "Pornography Usage (Duration)"),
         name = str_replace_all(name, "COPS_Frequency_2", "Pornography Usage (Frequency)")) |> 
  ggplot(aes(x = value, fill=Sex)) +
  geom_bar() +
  scale_y_continuous(expand = c(0, 0), breaks= scales::pretty_breaks()) +
  scale_fill_manual(values = c("Male"= "#64B5F6", "Female"= "#F06292")) +
  labs(title = "Sexual Profile of Participants") +
  theme_modern(axis.title.space = 15) +
  theme(
    plot.title = element_text(size = rel(1.2), face = "bold", hjust = 0),
    plot.subtitle = element_text(size = rel(1.2), vjust = 7),
    axis.text.y = element_text(size = rel(1.1)),
    axis.text.x = element_text(size = rel(1.1), angle = 45, hjust = 1),
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  ) +
  facet_wrap(~name, scales = "free")
p_sexprofile

Code
p_language <- df |>
  ggplot(aes(x = Language_Level)) +
  geom_bar() +
  scale_y_continuous(expand = c(0, 0), breaks= scales::pretty_breaks()) +
  labs(x = "Level", y = "Number of Participants", title = "Language Level") +
  theme_modern(axis.title.space = 15) +
  theme(
    plot.title = element_text(size = rel(1.2), face = "bold", hjust = 0),
    plot.subtitle = element_text(size = rel(1.2), vjust = 7),
    axis.text.y = element_text(size = rel(1.1)),
    axis.text.x = element_text(size = rel(1.1))
  )

p_expertise <- df |>
  ggplot(aes(x = AI_Knowledge)) +
  geom_bar() +
  scale_y_continuous(expand = c(0, 0), breaks= scales::pretty_breaks()) +
  labs(x = "Level", y = "Number of Participants", title = "AI-Expertise") +
  theme_modern(axis.title.space = 15) +
  theme(
    plot.title = element_text(size = rel(1.2), face = "bold", hjust = 0),
    plot.subtitle = element_text(size = rel(1.2), vjust = 7),
    axis.text.y = element_text(size = rel(1.1)),
    axis.text.x = element_text(size = rel(1.1))
  )

p_language | p_expertise

Code
p_country /
  (p_age + p_edu)

Beliefs about Artificial Information Technology (BAIT)

This section pertains to the validation of the BAIT scale measuring beliefs and expectations about artificial creations.

Exploratory Factor Analysis

Code
bait <- df |> 
  select(starts_with("BAIT_"), -BAIT_Duration) |> 
  rename_with(function(x) gsub("BAIT_\\d_", "", x))


cor <- correlation::correlation(bait, redundant = TRUE) |> 
  correlation::cor_sort() |> 
  correlation::cor_lower()

clean_labels <- function(x) {
  x <- str_remove_all(x, "BAIT_") |> 
    str_replace_all("_", " - ")
}

cor |> 
  mutate(val = paste0(insight::format_value(r), format_p(p, stars_only=TRUE))) |>
  mutate(Parameter2 = fct_rev(Parameter2)) |>
  mutate(Parameter1 = fct_relabel(Parameter1, clean_labels),
         Parameter2 = fct_relabel(Parameter2, clean_labels)) |> 
  ggplot(aes(x=Parameter1, y=Parameter2)) +
  geom_tile(aes(fill = r), color = "white") +
  geom_text(aes(label = val), size = 3) +
  labs(title = "Correlation Matrix",
       subtitle = "Beliefs about Artificial Information Technology (BAIT)") +
  scale_fill_gradient2(
    low = "#2196F3",
    mid = "white",
    high = "#F44336",
    breaks = c(-1, 0, 1),
    guide = guide_colourbar(ticks=FALSE),
    midpoint = 0,
    na.value = "grey85",
    limit = c(-1, 1))  + 
  theme_minimal() +
  theme(legend.title = element_blank(),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1))

Code
n <- parameters::n_factors(bait, package = "nFactors")
plot(n)

Code
efa <- parameters::factor_analysis(bait, cor=cor(bait), n=3, rotation = "oblimin", 
                                   sort=TRUE, scores="tenBerge", fm="ml")
plot(efa)

Code
display(efa)
Rotated loadings from Factor Analysis (oblimin-rotation)
Variable ML3 ML1 ML2 Complexity Uniqueness
EnvironmentReal 0.66 0.03 -0.05 1.01 0.55
VideosIssues 0.56 -0.22 0.21 1.63 0.60
ImagesRealistic 0.52 0.06 -0.18 1.27 0.65
ImitatingReality 0.49 0.03 -0.18 1.28 0.68
VideosRealistic 8.59e-05 0.99 0.02 1.00 5.00e-03
TextRealistic 0.15 -0.03 -0.67 1.11 0.45
TextIssues 0.08 0.06 0.64 1.05 0.60
ImagesIssues 0.02 0.26 0.28 2.01 0.84

The 3 latent factors (oblimin rotation) accounted for 45.10% of the total variance of the original data (ML3 = 16.89%, ML1 = 14.29%, ML2 = 13.92%).

Confirmatory Factor Analysis

Code
m1 <- lavaan::cfa(
  "G =~ ImitatingReality + EnvironmentReal + VideosIssues + TextIssues + VideosRealistic + ImagesRealistic + ImagesIssues + TextRealistic", 
  data=bait)
m2 <- lavaan::cfa(
  "Images =~ ImitatingReality + EnvironmentReal + ImagesRealistic + ImagesIssues + VideosIssues + VideosRealistic
  Text =~ TextIssues + TextRealistic", 
  data=bait)
m3 <- lavaan::cfa(
  "Images =~ ImitatingReality + EnvironmentReal + ImagesRealistic + ImagesIssues
  Videos =~ VideosIssues + VideosRealistic
  Text =~ TextIssues + TextRealistic", 
  data=bait)
m4 <- lavaan::cfa(
  "Environment =~ ImitatingReality + EnvironmentReal 
  Images =~ ImagesRealistic + ImagesIssues
  Videos =~ VideosIssues + VideosRealistic
  Text =~ TextIssues + TextRealistic", 
  data=bait)
m5 <- lavaan::cfa(efa_to_cfa(efa, threshold="max"), data=bait)


# bayestestR::bayesfactor_models(m1, m2)
lavaan::anova(m1, m2, m3, m4, m5)
Warning in lavTestLRT(object = object, ..., model.names = NAMES): lavaan WARNING:
    Some restricted models fit better than less restricted models;
    either these models are not nested, or the less restricted model
    failed to reach a global optimum. Smallest difference =
    -4.36016981458226

Chi-Squared Difference Test

   Df     AIC     BIC   Chisq Chisq diff   RMSEA Df diff Pr(>Chisq)    
m4 14 -96.253 -15.289  58.784                                          
m3 17 -89.518 -19.594  71.519     12.735 0.10524       3   0.005246 ** 
m5 18 -61.411   4.832 101.626     30.106 0.31518       1  4.090e-08 ***
m2 19 -67.772  -5.209  97.266     -4.360 0.00000       1   1.000000    
m1 20 -22.398  36.485 144.639     47.373 0.39783       1  5.867e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
display(parameters::parameters(m3, standardize = TRUE))
# Loading
Link Coefficient SE 95% CI z p
Images =~ ImitatingReality 0.58 0.05 (0.47, 0.68) 10.91 < .001
Images =~ EnvironmentReal 0.61 0.05 (0.51, 0.71) 11.85 < .001
Images =~ ImagesRealistic 0.62 0.05 (0.52, 0.72) 12.24 < .001
Images =~ ImagesIssues -0.28 0.06 (-0.40, -0.15) -4.28 < .001
Videos =~ VideosIssues 0.77 0.09 (0.60, 0.95) 8.72 < .001
Videos =~ VideosRealistic -0.48 0.07 (-0.62, -0.35) -6.95 < .001
Text =~ TextIssues 0.49 0.07 (0.35, 0.63) 6.91 < .001
Text =~ TextRealistic -0.95 0.11 (-1.16, -0.74) -8.87 < .001
# Correlation
Link Coefficient SE 95% CI z p
Images ~~ Videos 0.61 0.09 (0.44, 0.78) 7.11 < .001
Images ~~ Text -0.53 0.08 (-0.70, -0.37) -6.41 < .001
Videos ~~ Text -0.16 0.08 (-0.31, -6.32e-03) -2.04 0.041

Exploratory Graph Analysis (EGA) is a recently developed framework for psychometric assessment, that can be used to estimate the number of dimensions in questionnaire data using network estimation methods and community detection algorithms, and assess the stability of dimensions and items using bootstrapping.

Unique Variable Analysis (UVA)

Unique Variable Analysis (Christensen, Garrido, & Golino, 2023) uses the weighted topological overlap measure (Nowick et al., 2009) on an estimated network. Values greater than 0.25 are determined to have considerable local dependence (i.e., redundancy) that should be handled (variables with the highest maximum weighted topological overlap to all other variables (other than the one it is redundant with) should be removed).

Code
uva <- EGAnet::UVA(data = bait, cut.off = 0.3)
uva
Variable pairs with wTO > 0.30 (large-to-very large redundancy)

        node_i     node_j   wto
 TextRealistic TextIssues 0.343

----

Variable pairs with wTO > 0.25 (moderate-to-large redundancy)

----

Variable pairs with wTO > 0.20 (small-to-moderate redundancy)

          node_i       node_j   wto
 VideosRealistic VideosIssues 0.213
Code
uva$keep_remove
$keep
[1] "TextIssues"

$remove
[1] "TextRealistic"

Networks

Code
ega <- list()
for(model in c("glasso", "TMFG")) {
  for(algo in c("walktrap", "louvain")) {
    for(type in c("ega", "ega.fit", "riEGA")) {  # "hierega"
      if(type=="ega.fit" & algo=="louvain") next  # Too slow
      ega[[paste0(model, "_", algo, "_", type)]] <- EGAnet::bootEGA(
        data = bait,
        seed=123,
        model=model,
        algorithm=algo,
        EGA.type=type,
        type="resampling",
        plot.itemStability=FALSE,
        verbose=FALSE)
      }
   }
}
The random-intercept model converged. Wording effects likely. Results are only valid if data are unrecoded.
The random-intercept model converged. Wording effects likely. Results are only valid if data are unrecoded.
The random-intercept model converged. Wording effects likely. Results are only valid if data are unrecoded.
The random-intercept model converged. Wording effects likely. Results are only valid if data are unrecoded.
Code
EGAnet::compare.EGA.plots(
  ega$glasso_walktrap_ega, ega$glasso_walktrap_ega.fit,
  ega$glasso_louvain_ega, ega$TMFG_louvain_ega,
  ega$glasso_louvain_riEGA, ega$glasso_walktrap_riEGA,
  ega$TMFG_walktrap_ega, ega$TMFG_walktrap_ega.fit,
  ega$TMFG_louvain_riEGA, ega$TMFG_walktrap_riEGA, 
  labels=c("glasso_walktrap_ega", "glasso_walktrap_ega.fit",
           "glasso_louvain_ega", "TMFG_louvain_ega",
           "glasso_louvain_riEGA", "glasso_walktrap_riEGA",
           "TMFG_walktrap_ega", "TMFG_walktrap_ega.fit",
           "TMFG_louvain_riEGA", "TMFG_walktrap_riEGA"),
  rows=5,
  plot.all = FALSE)$all

Structure Stability

Figures shows how often each variable is replicating in their empirical structure across bootstraps.

Code
patchwork::wrap_plots(lapply(ega, plot), nrow = 4)

Final Model

Code
ega_final <- ega$glasso_walktrap_riEGA$EGA
plot(ega_final)

Code
ega_scores <-  EGAnet::net.scores(bait, ega_final)$scores$std.scores |> 
  as.data.frame() |> 
  setNames(c("EGA_Text", "EGA_Image", "EGA_Videos")) 
# Merge with data
scores <- lavaan::predict(m3) |> 
  as.data.frame() |> 
  datawizard::data_addprefix("CFA_") |> 
  # data_rename(c("ML1", "ML2"), c("BAIT_SEM1", "BAIT_SEM2")) |> 
  cbind(ega_scores) |> 
  mutate(Participant = df$Participant)

scores$BAIT_Videos <- (bait$VideosRealistic + (1 - bait$VideosIssues)) / 2
scores$BAIT_Images <- (bait$ImagesRealistic + (1 - bait$ImagesIssues) + bait$ImitatingReality + bait$EnvironmentReal) / 4
scores$BAIT_Text <- (bait$TextRealistic + (1 - bait$TextIssues)) / 2

df <- full_join(df, scores, by="Participant")

We computed two type of general scores for the BAIT scale, an empirical score based on the average of observed data (of the most loading items) and a model-based score as predicted by the structural model. The first one gives equal weight to all items (and keeps the same [0-1] range), while the second one is based on the factor loadings and the covariance structure.

Code
correlation::cor_test(scores, "BAIT_Images", "CFA_Images") |> 
  plot() +
  ggside::geom_xsidedensity(aes(x=BAIT_Images), color="grey", linewidth=1) +
  ggside::geom_ysidedensity(aes(y=CFA_Images), color="grey", linewidth=1) +
  ggside::scale_xsidey_continuous(expand = c(0, 0)) +
  ggside::scale_ysidex_continuous(expand = c(0, 0)) +
  ggside::theme_ggside_void() +
  theme(ggside.panel.scale = .1) 

While the two correlate substantially, they have different benefits. The empirical score has a more straightforward meaning and is more reproducible (as it is not based on a model fitted on a specific sample), the model-based score takes into account the relative importance of the contribution of each item to their factor.

Code
table <- correlation::correlation(scores) |> 
  summary()

format(table) |> 
  datawizard::data_rename("Parameter", "Variables") |> 
  gt::gt() |> 
  gt::cols_align(align="center") |> 
  gt::tab_options(column_labels.font.weight="bold")
Variables BAIT_Text BAIT_Images BAIT_Videos EGA_Videos EGA_Image EGA_Text CFA_Text CFA_Videos
CFA_Images 0.52*** 0.91*** -0.56*** 0.14 0.92*** 0.29*** -0.63*** 0.74***
CFA_Videos 0.15 0.56*** -0.92*** 0.31*** 0.56*** 0.11 -0.21**
CFA_Text -0.89*** -0.45*** 0.17 0.08 -0.45*** -0.47***
EGA_Text 3.94e-03 0.16 -0.08 0.02 0.17
EGA_Image 0.37*** 0.98*** -0.37*** 0.05
EGA_Videos -0.11 0.02 2.62e-03
BAIT_Videos -0.14 -0.38***
BAIT_Images 0.37***

Corrrelation with GAAIS

Code
table <- correlation::correlation(
  select(scores, starts_with("BAIT_")), 
  select(df, starts_with("GAAIS")),
  bayesian=TRUE) |> 
  summary()

format(table) |> 
  datawizard::data_rename("Parameter", "Variables") |> 
  gt::gt() |> 
  gt::cols_align(align="center") |> 
  gt::tab_options(column_labels.font.weight="bold")
Variables GAAIS_Negative_9 GAAIS_Positive_17 GAAIS_Positive_12 GAAIS_Negative_15 GAAIS_Positive_7 GAAIS_Negative_10
BAIT_Videos -0.11* 0.02 0.11 -0.21*** 0.16** -0.15**
BAIT_Images 0.16** 0.22*** 0.24*** 0.03 0.14** -0.03
BAIT_Text 0.07 0.21*** 0.15** -0.12* 0.24*** -0.16**

AI-Expertise

Code
df |> 
  ggplot(aes(x=as.factor(AI_Knowledge), y=BAIT_Images)) +
  geom_boxplot()

Code
# m <- betareg::betareg(BAIT ~ AI_Knowledge, data=df)
m <- lm(BAIT_Images ~ poly(AI_Knowledge, 2), data=df)
# m <- brms::brm(BAIT ~ mo(AI_Knowledge), data=df, algorithm = "meanfield")
# m <- brms::brm(BAIT ~ AI_Knowledge, data=dfsub, algorithm = "meanfield")
display(parameters::parameters(m))
Parameter Coefficient SE 95% CI t(290) p
(Intercept) 0.69 9.22e-03 (0.67, 0.71) 74.89 < .001
AI Knowledge (1st degree) -0.10 0.16 (-0.41, 0.21) -0.61 0.542
AI Knowledge (2nd degree) 0.41 0.16 (0.10, 0.72) 2.59 0.010
Code
marginaleffects::predictions(m, by=c("AI_Knowledge"), newdata = "marginalmeans") |> 
  as.data.frame() |> 
  ggplot(aes(x=AI_Knowledge, y=estimate)) +
  geom_jitter2(data=df, aes(y=BAIT_Images), alpha=0.2, width=0.1) +
  geom_line(aes(group=1), position = position_dodge(width=0.2)) +
  geom_pointrange(aes(ymin = conf.low, ymax=conf.high), position = position_dodge(width=0.2)) +
  theme_minimal() +
  labs(x = "AI-Knowledge", y="BAIT Score")

Gender and Age

Code
# m <- betareg::betareg(BAIT ~ Sex / Age, data=df, na.action=na.omit)
m <- lm(BAIT_Images ~ Sex / Age, data=df)
display(parameters::parameters(m))
Parameter Coefficient SE 95% CI t(289) p
(Intercept) 0.65 0.04 (0.57, 0.74) 14.77 < .001
Sex (Male) 0.05 0.06 (-0.06, 0.16) 0.95 0.342
Sex (Female) × Age 2.42e-03 1.53e-03 (-5.94e-04, 5.43e-03) 1.58 0.115
Sex (Male) × Age -7.14e-04 8.46e-04 (-2.38e-03, 9.51e-04) -0.84 0.399

Belief in the Instructions

Code
glm(Feedback_LabelsIncorrect ~ BAIT_Images * AI_Knowledge, 
    data=mutate(df, Feedback_LabelsIncorrect = ifelse(Feedback_LabelsIncorrect=="True", 1, 0)), 
    family="binomial") |> 
  parameters::parameters() |> 
  display(title="Predicting 'Labels are Incorrect'")
Predicting ‘Labels are Incorrect’
Parameter Log-Odds SE 95% CI z p
(Intercept) -0.33 1.73 (-3.72, 3.09) -0.19 0.849
BAIT Images -1.06 2.37 (-5.81, 3.53) -0.45 0.656
AI Knowledge 0.29 0.47 (-0.64, 1.23) 0.61 0.539
BAIT Images × AI Knowledge -0.13 0.65 (-1.39, 1.16) -0.20 0.843
Code
glm(Feedback_LabelsReversed ~ BAIT_Images * AI_Knowledge, 
    data=mutate(df, Feedback_LabelsReversed = ifelse(Feedback_LabelsReversed=="True", 1, 0)), 
    family="binomial") |> 
  parameters::parameters() |> 
  display(title="Predicting 'Labels are reversed'")
Predicting ‘Labels are reversed’
Parameter Log-Odds SE 95% CI z p
(Intercept) -0.12 2.85 (-5.75, 5.44) -0.04 0.966
BAIT Images -2.81 4.01 (-10.93, 4.76) -0.70 0.484
AI Knowledge -0.45 0.81 (-2.04, 1.13) -0.55 0.581
BAIT Images × AI Knowledge 0.49 1.13 (-1.68, 2.72) 0.43 0.666
Code
glm(Feedback_CouldDiscriminate ~ BAIT_Images * AI_Knowledge, 
    data=mutate(df, Feedback_CouldDiscriminate = ifelse(Feedback_CouldDiscriminate=="True", 1, 0)), 
    family="binomial") |> 
  parameters::parameters() |> 
  display(title="Predicting 'Easy to discriminate'")
Predicting ‘Easy to discriminate’
Parameter Log-Odds SE 95% CI z p
(Intercept) -0.81 3.05 (-7.01, 5.01) -0.26 0.791
BAIT Images -1.85 4.06 (-9.89, 6.05) -0.46 0.648
AI Knowledge -0.80 0.88 (-2.52, 0.94) -0.90 0.367
BAIT Images × AI Knowledge 0.94 1.15 (-1.31, 3.19) 0.82 0.414
Code
glm(Feedback_CouldNotDiscriminate ~ BAIT_Images * AI_Knowledge, 
    data=mutate(df, Feedback_CouldNotDiscriminate = ifelse(Feedback_CouldNotDiscriminate=="True", 1, 0)), 
    family="binomial") |> 
  parameters::parameters() |> 
  display(title="Predicting 'Hard to discriminate'")
Predicting ‘Hard to discriminate’
Parameter Log-Odds SE 95% CI z p
(Intercept) -3.04 1.84 (-6.78, 0.49) -1.65 0.100
BAIT Images 4.68 2.50 (-0.07, 9.81) 1.87 0.061
AI Knowledge -0.01 0.51 (-1.01, 1.00) -0.02 0.982
BAIT Images × AI Knowledge -0.18 0.69 (-1.56, 1.16) -0.26 0.795
Code
glm(Feedback_Fun ~ BAIT_Images * AI_Knowledge, 
    data=mutate(df, Feedback_Fun = ifelse(Feedback_Fun=="True", 1, 0)), 
    family="binomial") |> 
  parameters::parameters() |> 
  display(title="Predicting 'Fun'")
Predicting ‘Fun’
Parameter Log-Odds SE 95% CI z p
(Intercept) 7.43e-03 1.68 (-3.32, 3.31) 4.42e-03 0.996
BAIT Images 0.75 2.29 (-3.71, 5.32) 0.33 0.742
AI Knowledge -0.13 0.47 (-1.05, 0.79) -0.28 0.777
BAIT Images × AI Knowledge 0.16 0.63 (-1.09, 1.41) 0.26 0.798
Code
glm(Feedback_Boring ~ BAIT_Images * AI_Knowledge, 
    data=mutate(df, Feedback_Boring = ifelse(Feedback_Boring=="True", 1, 0)), 
    family="binomial") |> 
  parameters::parameters() |> 
  display(title="Predicting 'Boring'")
Predicting ‘Boring’
Parameter Log-Odds SE 95% CI z p
(Intercept) -0.18 2.06 (-4.21, 3.89) -0.09 0.932
BAIT Images -2.18 2.90 (-8.04, 3.36) -0.75 0.452
AI Knowledge 0.01 0.56 (-1.08, 1.11) 0.03 0.979
BAIT Images × AI Knowledge 0.14 0.78 (-1.38, 1.69) 0.18 0.859

Save

write.csv(df, "../data/data_participants.csv", row.names = FALSE)
write.csv(dftask, "../data/data.csv", row.names = FALSE)